System for the detection and the early prediction of the approaching of exacerbations in patients suffering from chronic obstructive broncopneumaty

ABSTRACT

The present invention concerns a system (S) for the detection and early warning of the incoming of acute events in patients with chronic obstructive pulmonary disease, comprising: at least one device (D) for the detection of physiological parameters (R), that can be applied to said patient to be monitored; at least one timer for detecting time intervals, such as date and time, associated with said detected physiological parameters (R); at least one emission device of sound and/or visual alarm signals capable of emitting an sound and/or visual output warning signal, associated with said physiological detected parameters (R); a control logic unit (C), connectable to said at least one device (D) and at least one timer, and capable of controlling said at least one emission signals device, suitable to receive in input said physiological detected parameters (R) and said time intervals, said control logic unit (C) being provided with a processing program, in which thresholds of predetermined values reached by said physiological parameters (R) are initially stored, which runs the following steps: associating said detected physiological parameters (R) with the time intervals, in which the detection has taken place; for every detection time instant, sending said physiological parameters (R) measured at a statistical indices calculation algorithm; comparing said statistical indexes obtained in the preceding step with said predetermined threshold and activating said at least one signals emission device for the emission of a sound and/or visual warning signal if at least one of said statistical indexes exceeds said corresponding predetermined threshold.

The present invention relates to a system for the detection and theearly prediction of the approaching of exacerbations in patientssuffering from chronic obstructive broncopneumaty.

Chronic obstructive broncopneumaty or COPD is a chronic pulmonarydisease characterized by bronchial obstruction, with partial or notreversible airflow limitation, slowly progressive, caused by chronicinflammation of the airways and of the pulmonary parenchyma.

It is considered the fourth cause of death in the US and the fifth inthe world.

Patients with this disease have periodic exacerbations.

Exacerbations phases are more or less long and more or less rapid onset,during which COPD or asthma symptoms get worse, and then the patient, ondoctor's advice, should change or intensify the medication that isassuming.

Exacerbations must be reported as soon as possible to the doctorbecause, especially if not quickly and properly treated, can lead toserious consequences for the asthma or COPD patient, such ashospitalization or even death.

The drugs used to regular daily treat these diseases have as their mainpurpose to prevent exacerbations.

The invention thus relates to a system of the above kind, studied andrealized especially to early detect and report the emergence of saidexacerbations or clinically critical situations, called “worrisomeevents”, which could lead to deterioration of health of the COPDsubject.

In the following, the description will be directed to system fordetection and early warning of exacerbations, but it is clear that thesame should not be considered limited to this specific use.

The system in fact can also be employed for the remote multiparametricor monoparametric monitoring in stability conditions.

Currently there are in the literature algorithms that evaluate whether apatient is at COPD exacerbations risk.

An algorithm known in the art takes into account the measurements of adevice that detects physiological parameters during a time window,possibly variable, of 30 days duration; then the method determines aregression line a in the plane defined by the Cartesian coordinates oftime and SpO₂ values.

The coefficient α of the regression line a is compared with a referencevalue α₀ .

Assuming α₀=-0,0737, and defining a function

${F(a)} = \left\{ \begin{matrix}{1,} & {{{se}\mspace{14mu} a} < a_{0}} \\{0,} & {{{se}\mspace{14mu} a} > a_{0}}\end{matrix} \right.$

if the value obtained is less than α₀ , the result obtained is less than1, indicating that there is an exacerbation phase; instead, if theresult obtained is greater than α₀, the result obtained will be equal to0, indicating that the patient is not at exacerbation risk.

The state of the art only considers the risk for a patient affected byCOPD to get close to an exacerbation event, without considering otherimportant aspects of the health of the patient as possible dyspnea andtachycardia.

Furthermore, in the prior art a fixed threshold for discriminatingwhether an event corresponds to an exacerbation is or not is adopted.

For the prediction of the exacerbation is used only SpO₂ parameter,while in the literature it has been shown that the predictive capabilityof exacerbations improves if the trend of both oxygen saturation SpO₂and both heart rate are monitored.

The mathematical expression upon which the known algorithm is based,takes into account only the existing connection between time and oxygensaturation (SpO₂), assuming that between these two parameters there is alinear dependence relationship.

Furthermore, the model according to the prior art does not take intoaccount the personal physiological characteristics of the patient, suchas the time average of the oxygen saturation (SpO₂), which is aninformation necessary for a correct estimate of the trend of saidpatient health state.

In the light of the above it is, therefore, object of the presentinvention to provide a system for the detection and the early predictionof the approaching of exacerbations in patients suffering from chronicobstructive broncopneumaty, taking account the physiological parametersof the patient.

A further object of the present invention is to provide a system, whichtakes into account time variations of the measurements of physiologicalparameters of the patient.

It is therefore specific object of the present invention a system forthe detection and early warning of the incomingacute events in patientswith chronic obstructive pulmonary disease, comprising: at least onedevice for the detection of physiological parameters, that can beapplied to said patient to be monitored; at least one timer fordetecting time intervals, such as date and time, associated with saiddetected physiological parameters; at least one emission device of soundand/or visual alarm signals capable of emitting an sound and/or visualoutput warning signal, associated with said physiological detectedparameters; a control logic unit, connectable to said at least onedevice and at least one timer, and capable of controlling said at leastone emission signals device, suitable to receive in input saidphysiological detected parameters and said time intervals, said controllogic unit being provided with a processing program, in which thresholdsof predetermined values reached by said physiological parameters areinitially stored, which runs the following steps: associating saiddetected physiological parameters with the time intervals, in which thedetection has taken place; for every detection time instant, sendingsaid physiological parameters measured at a statistical indicescalculation algorithm; comparing said statistical indexes obtained inthe preceding step with said predetermined threshold and activating saidat least one signals emission device for the emission of a sound and/orvisual warning signal if at least one of said statistical indexesexceeds said corresponding predetermined threshold.

Further according to the invention, said at least one device is a pulseoximeter that detects the following physiological data: hemoglobinsaturation (SpO); and heart rate (HR);

in the following preset time frames, and scanned by said timer: morninghours interval (C_(morning)); afternoon hours interval (C_(Afternoon));evening hours interval (C_(Evening)).

Still according to the invention, said logic control unit comprises afirst unit configured to carry out said association of said detectedphysiological parameters with the time frames in which the detection hastaken place, obtaining the following registrations: morningregistrations (X_(SpO) ₂ ^(morning), X_(HR) ^(morning)); afternoonregisterations (X_(SpO) ₂ ^(afternoon), X_(HR) ^(afternoon)); andevening registrations (X_(SpO) ₂ ^(evening), X_(HR) ^(evening)); and asecond unit, comprising a neural network implemented with a BinaryFinite State Machine (BFSM), configured to process said grouped inputdata, according to said processing program.

Preferably according to the invention, said Binary Finite State Machine(BMSF) runs a first calibration step for setting said predeterminedthresholds of values P=(ϵ, Weight SpO₂, WeightHR, κ, λ), representingthe typical trends of said physiological parameters of hemoglobinsaturation (SpO₂) and heart rate (HR) measurable from said patient to bemonitored, and a second learning step of said physiological parametersof hemoglobin saturation (SpO₂) and heart rate (HR), wherein said BinaryFinite State Machine (BMSF) learns the trend of said hemoglobinsaturation (SpO₂) and heart rate (HR) of said specific patient to bemonitored in said preset time frames of the morning (C_(Morning)), inthe afternoon (C_(Afternoon)) and evening (C_(Evening)).

Further according to the invention, said processing program performs thefollowing steps for the calculation of said statistical indices:calculation of the average of said registrations of the morning (X_(SpO)₂ ^(morning), X_(HR) ^(morning)), afternoon (X_(SpO) ₂ ^(afternoon),X_(HR) ^(afternoon)) and evening (X_(SpO) ₂ ^(evening), X_(HR)^(evening)), obtaining the values of the average of the morning(Average_(morning(SpO) ₂ ₎, Average_(morning(HR))), afternoon(Average_(afternoon(SpO) ₂ ₎Average_(afternoon(HR))) and evening(Average_(evening(SpO) ₂ ₎, Average_(evening(HR))); calculating thestandard deviation of said registrations of the morning (X_(SpO) ₂^(morning), X_(HR) ^(morning)), afternoon (X_(SpO) ₂ ^(afternoon),X_(HR) ^(afternoon)) and evening (X_(SpO) ₂ ^(evening), X_(HR)^(evening)), obtaining the values of the standard deviation of themorning (Devst_(morning(SpO) ₂ ₎, Devst_(morning(HR))), afternoon(Devst_(afternoon(SpO) ₂₎ , Devst_(afternoon(HR))) and evening(Devst_(evening(SpO) ₂ ₎, Devst_(evening(HR))); calculating a weightedcombination, or Oximetry Weighted Score (WOS) for each class time:

${{WOS}\left( {X_{{SPO}_{2}},X_{HR}} \right)} = \frac{\begin{matrix}{{- {{Weight}_{{SPO}_{2}}\left( \frac{X_{{SPO}_{2}} - {{Average}\left( {SPO}_{2} \right)}}{{Devst}\left( {SPO}_{2} \right)} \right)}} +} \\{{Weight}_{HR}\left( \frac{X_{HR} - {{Average}({HR})}}{{Devst}({HR})} \right)}\end{matrix}}{{Weight}_{{SPO}_{2}} + {Weight}_{HR}}$

comparing said statistical indexes obtained in previous steps with saidpredetermined values thresholds.

Still according to the invention, said Binary Finite State Machine(BMSF) evolves in the following states associated to critical warningevents: HR Alarm when for two consecutive registrations (R) thecondition X_(HR))Average_(HR)+K*Devst_(HR) occurs, in which the value Kis determined in said self-learning step; Oximetryscore Punctual Alarmwhen the condition WOS(X_(SpO) ₂ , X_(HR)))WOS(X_(SpO) ₂ Average(HR))occurs; SpO₂ Alarm when X_(SpO) ₂ ≤γ, conγ∈[80,95], Missing Data Alarmin the case of two missing consecutive registrations (R), Alarm Oximetryscore associated with an alarm place reached when recording fulfils oneof the following conditions:

Warning1: WOS(X _(SpO) ₂ , X _(HR))>WOS(SPO_(2critical)+ϵ,Average(HR))*B ₁ ^(λ)

Warning2: WOS(X _(SpO) ₂ , X _(HR))>WOS(SPO_(2critical)+ϵ,Average(HR))*B ₂ ^(λ)

Warning3: WOS(X _(SpO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₃ ⁸⁰

AllarmWarrisomeOximctryScore:WOS(X _(SPO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B ₃ ^(λ)

wherein B1, B2, B3 c [0, 1] and SpO_(2 critical) is the critical valueof oxygen saturation, normally equal to 0.9%.

Preferably according to the invention, said HR Alarm, OximetryscorePunctual Alarm, SpO₂ Alarm, Missing Data Alarm and Alarm Oximetry scorestates are provided as input to said emission device of sound and/orvisual alarm signals, which emits sound and/or visual signals for eachcorresponding state.

Further according to the invention, said system could comprise aplurality of sensors capable of detecting further physiologicalparameters from said monitored patient.

Still according to the invention, said sensors comprise an accelerometerfor detecting movements of said patient and/or a spirometer for thedetection of pulmonary volume of said patient to provide input to saidcontrol logic unit.

Preferably according to the invention, in said calibration step of saidFinite State Machine (BMSF), a self-learning algorithm is used, whichconfiguration parameters P=(ϵ, Weight SpO₂, WeightHR, κ, λ) can be setby a user.

Further according to the invention, said Finite State Machine (MSFB)learns said parameters P according to the following steps: providing avalidation set of registrations (R) on which, fixed a possibleconfiguration of the parameters {circumflex over (P)}, the algorithm istested and the weighted accuracy acc_(weighted) is estimated; testingsaid parameters, by setting the data value WeightHR equal to 1, whilethe value of the datum Weight SpO₂ can vary between 1 and 20;determining the set of said parameters {circumflex over (P)} byperforming the Cartesian product of the possible combinations ofparameters P=(ϵ, Weight SpO₂, WeightHR, κ, λ); selecting the parameterconfiguration P* to be used by said system (S), by selecting acombination of said parameters that maximizes the weighted accuracyvalue according to the formula P* =arg max_(t) (acc_(weighted)({circumflex over (P)}_(t))).

Still according to the invention, said parameter λ∈[0.1, . . . , i, . .. , 0.09]∪[1, . . . , j, . . . , 10], with i ∈

and j∈

and such that λ₊₁−λ₁=0.01 and said parameter κ∈[1, . . . , i . . . , 10]with i ∈

.

The present invention will be now described, for illustrative but notlimitative purposes, according to its preferred embodiments, withparticular reference to the figures of the enclosed drawings, wherein:

FIG. 1 shows a block diagram of the system for the detection and earlywarning of the incoming acute events in patients with chronicobstructive pulmonary disease;

FIG. 2 shows the operation of a part of the system according to theinvention;

FIG. 3 shows a graph relating to the priority levels associated withcritical events detected by the system according to the invention;

FIG. 4 shows a possible management of the priority levels of criticalevents; and

FIG. 5 shows a block diagram of the operation of the system according tothe invention.

In the various figures, similar parts will be indicated by the samereference numbers.

Referring to the enclosed figures, it is seen that the system S for thedetection and prediction of acute events in patients suffering fromchronic obstructive broncopneumaty, object of the present invention,comprises a device D for the acquisition of physiological data of apatient suffering from chronic obstructive broncopneumaty, such as apulse oximeter.

The data detected by said device D are mainly the saturation ofhemoglobin, also called SpO₂, and the heart rate, also called Heart Rateor HR, for said patient.

Said system S includes a timer for the detection of temporal data, suchas date and time are associated with said physiological data, as it willbe described in detail hereinafter.

Said system S may comprise a plurality of sensors, not shown in thefigure, capable of detecting further physiological parameters from saidpatient, such as an accelerometer for detecting movements of saidpatient and a spirometer for the detection of pulmonary volume of saidpatient.

Said system S also comprises a control logic unit C which receives ininput said physiological data and said time data, hereinafter referredto as input data, acquired from said device D, from said plurality ofsensors and from said timer, processes said data input in accordancewith a predetermined program, which will be described in detailhereinafter, and supplies processed data at the output, hereinafterreferred to as output data.

Said control logic unit C comprises a first part C, which carries out apre-processing of said input data, and a second part C, comprising arecognition algorithm of acute events, implemented by means of a BinaryFinite State Machine, or even BMSF, that performs the processing of saidpre-processed input data, as will it be described in detail hereinafter.

Said system S also comprises a device for the emission of sound and/orvisual warning alarm, not shown, which receives in input said outputdata from said control logic unit C, and outputs warning signals,corresponding to said output data according to a predetermined logic,which will be described in detail hereinafter.

Said data input to said control logic unit C are in particular four, thevalue of the hemoglobin saturation percentage, the value of the heartrate HR measured by said pulse oximeter and the date and time at whichthe measurement or registration R takes place, provided by said timer.

Such input data are formally designated by the triad (X_(SPO2),X_(HR)XHR, t), where the value t indicates both the date and time of theregistrations R.

Registrations R can be made at different times of day, according towhich said input data are classified.

In particular, three possible hourly classes are defined:

-   -   morning class, indicated by C_(morning), containing all the        registrations R in the morning if the registration time belongs        to the time slot of the morning, said registrations are        indicated by (X_(SpO) ₂ ^(morning), X_(HR) ^(morning));    -   afternoon class, indicated by C_(afternoon) containing all the        registrations R in the afternoon if the registration time        belongs to the time slot of the afternoon, said registrations        are indicated by (X_(SpO) ₂ ^(afternoon), X_(HR) ^(afternoon));    -   evening class, indicated by C_(evening), containing all the        registrations R in the evening if the registration time belongs        to the time slot of the evening, said registrations are        indicated by (X_(SpO) ₂ ^(evening), X_(HR) ^(evening)).

Said time slots of each class are set and adapted to the specific needsof the patient and may be variable both in number and in duration.

Said first part C carries out the association between each registrationR and the hourly class to which they belong.

Said data output from said control logic unit C consist in a mean andstandard deviation of a time window of defined amplitude, for example 10days, of each of said input data, which are indicated with the followingsymbols:

(Average_(morning SpO) ₂ ₎, Devst_(morning(SpO) ₂ ₎,(Average_(morning(HR)), Devst_(morning(HR)));

(Average_(afternoon(SpO) ₂ ₎, Devst_(afternoon(SpO) ₂ ₎),(Average_(afternoon(HR)), Devst_(afternoon(HR)));

(Average_(evening(SpO) ₂ ₎, Devst_(evening(SpO) ₂ ₎),(Average_(evening(HR)), Devst_(evening(HR))).

Said output data from said control logic unit C are associated to saidsound and/or visual warning signals, as it will be described in detailhereinafter.

Said sound and/or visual warning signal emitted by said emission devicecan be listed as follows:

-   -   HR alarm associated with a HR registration over a predetermined        threshold, to which corresponds a possible sinus tachycardia;    -   score punctual Oximetry Alarm associated with a sharp variation        of the recordings of SpO₂ and HR over predetermined values,        which corresponds to a possible dyspnea;    -   SpO₂ Alarm related to a SpO₂ registration lower than a        predetermined threshold, to which a possible hypoxemia        corresponds;    -   Alarm missing data associated with two consecutive missing        recordings with respect to the established protocol by the        treating doctor of said patient, due to malfunction of said        pulse oximeter D, or due to a general ill state of the patient,        who is not able to perform the registration;    -   Alarm Oximetry score associated with a negative trend of SpO₂        and HR recordings, which corresponds to the incoming of possible        exacerbations of the disease.

According to the specific warning signal, said emission device signalsmay generate an alarm sound or a visual signal.

The operation of the system S described above is as follows.

When a patient suffering from chronic obstructive broncopneumaty decidesto use the system S described above, he agrees with his own treatingdoctor a protocol by which he determines the number of registrations Rto be made and the time intervals in which carrying out saidregistrations R.

In said system S it is possible both setting initial parameters andperforming a standard calibration step, so as to make said system Sadapted for the physiological parameters of said monitored patient, sothat it fits to the decisions process of the specialist of thepathology.

Therefore, in an initial calibration step of about 10 days duration,duration that is indicative and illustrative, which can be customized tothe specific characteristics of each patient, said pulse oximeter Dacquires repeatedly during the day physiological data of said patientmonitored, in particular the typical values of SpO₂ and HR of saidmonitored patient, in different time instants within the time frame of aday.

Said first part C₁ of said control logic unit C associates saidphysiological parameters to the initial time instants, in whichregistration was effected, so as to configure a typical trend of saidphysiological parameters in the time period of one day.

These initial physiological parameters, associated with time intervals,are then sent from said first part C₁ to said second part C₂ which,being a supervised learning algorithm, has the ability to learn andadapt to the physiological characteristics of said monitored patient,customizing then the specific physiological parameters of the patient atdifferent times of the day.

For this purpose the algorithm requires that the measurements acquiredin the aforesaid initial calibration stage are labeled, for example by aspecialist, so that the learning algorithm can adapt to the correctdecision-making process. The learning consists in optimizing thresholdparameters with respect to the above said physiological characteristics.

Then following said initial training period, said system S will continueto be modeled on the patient either through self-learning using theinput data, either through a possible intervention by the specialistdoctor by modifying the configuration parameters for a better responsefrom said system S.

As it will be described in detail hereinafter, said system S istherefore able to detect possible exacerbations, analyzing thecorrelated trend of SpO₂ and HR parameters, through a series of statetransitions of said system S that reflect the worsening of the patientmonitored until the reaching a place of alarm that shows an initialdeterioration of the physiological parameters of the monitored patient,allowing to identify the exacerbations in the early phase.

In particular, said BMSF comprised in said second part C₂ of saidcontrol logic unit C analyzes the measured values of SpO₂ and HR, tocreate a model of the time evolution of the state of health of saidmonitored patient.

A BMSF is a particular Petri net, which is a bipartite graph, whichdefines the following items:

token, shown in FIG. 2 with a black dot, corresponding to the state ofsaid monitored patient, at the moment when said registration R takesplace;

places, shown in FIG. 2 with white circles, corresponding to thepossible health states of said monitored patient, that are defined bythe normal state, in which measured physiological parameters of saidmonitored patient fall within normal ranges, alert state, in which thephysiological parameters measured by said monitored patient presenting anegative trend and alarm state in which measured physiologicalparameters of said monitored patient correspond to a critical state, forit is necessary a hospitalization of said monitored patient in anhospital, that are also called Worrisome events;

transitions, shown in FIG. 2 with black rectangles, corresponding toclinical evolutions from one place to another.

The places and transitions are called nodes and nodes are interconnectedby oriented arcs.

It is only possible connections between nodes of different types.

The dynamic evolution of said system S is represented through thepassage of a single token, from one place to the next one, when thetransition connecting the two places is enabled, i.e., when thecondition linked to that transition is verified.

As described above, those registrations R are pre-processed by saidfirst part C₁ of said control logic unit C and then are cataloguedaccording to the time in which they occurred, in three categories ofregistration, morning, afternoon and evening.

Subsequently, averages and standard deviations of the threeregistrations categories are calculated, for each hourly class saidsystem S calculates averages and standard deviations both for oxygensaturation measurements and for heart rate ones.

This means that the system S calculates independently the averages andstandard deviations of all recordings acquired between times, which maypossibly coincide with zero, and the second, which took place during thethree hourly classes, or in the morning, afternoon and evening.

Therefore, said system S calculates the above twelve parameters:

(Average_(morning (SpO) ₂ ₎, Devst_(morning (SpO) ₂ ₎),(Average_(morning(HR)), Devst_(morning(HR)));

(Average_(afternoon(SpO) ₂ ₎, Devst_(afternoon(SpO) ₂ ₎),(Average_(afternoon(HR)), Devst_(afternoon(HR)));

(Average_(evening(SpO) ₂ ₎, Devst_(evening(SpO) ₂ ₎),(Average_(evening(HR)), Devst_(evening(HR))).

Said system S, with each new registration updates the average andstandard deviation of the two measurements of the hourly class relatedto the new registration.

For each pair of measurements made in the same time slot at time t>t₂,indicated as (X_(SPO2), X_(HR)), it calculates a weighted combinationthat is called Weighted Oximetry Score (WOS):

${{WOS}\left( {X_{{SPO}_{2}},X_{HR}} \right)} = \frac{\begin{matrix}{{- {{Weight}_{{SPO}_{2}}\left( \frac{X_{{SPO}_{2}} - {{Average}\left( {SPO}_{2} \right)}}{{Devst}\left( {SPO}_{2} \right)} \right)}} +} \\{{Weight}_{HR}\left( \frac{X_{HR} - {{Average}({HR})}}{{Devst}({HR})} \right)}\end{matrix}}{{Weight}_{{SPO}_{2}} + {Weight}_{HR}}$

where SpO₂ and HR represent the sample registrations set used fortraining said system S.

After the pre-processing step by said first part C₁, the data are sentto said second part C₂, in particular to said BMSF of said control logicunit C proceeds starting from the place obtained from the previousevent.

Referring to FIG. 3, and in particular to the five vertical chains ofevents represented therein, the main network ways, from left to right,correspond to the above-described audio and/or visual warning signalsemitted by said emission device, which occur in the followingsituations:

-   -   HR Alarm is associated to a single place of alarm and is reached        when it occurs, for two consecutive events, the condition:        X_(HR))Average_(HR)+K*Devst_(HR), in which the value K is        determined in the self-learning step of said BMSF.    -   punctual Oximetryscore Alarm is associated with one place of        alarm reached when the condition WOS(X_(SpO) ₂ , (X_(SpO) ₂ ,        X_(HR)))WOS(X_(SpO) ₂ , Average(HR)) occurs.    -   SpO₂ Alarm is associated with a unique place of alarm that is        achieved when X_(SpO) ₂ ≤γ, where γ∈[80,95];    -   Alarm missing data is associated with a unique alarm place that        is reached at the first missing registration and from one        missing alarm place that is reached in case of two consecutive        missing measurements;    -   Alarm Oximetry score associated with a alarm place reached when        the registration verify one of the following conditions:

Warning1: WOS(X_(SPO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₁^(λ)

Warning2: WOS(X_(SPO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₂^(λ)

Warning3: WOS(X_(SPO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₃^(λ)

AllarmWarrisomeOximctryScore:WOS(X_(SPO) ₂ ,X_(HR))>WOS(SPO_(2critical), Average(HR)) where the parameters B1, B2,B3 vary between 0 and 1, for example can be set equal to 0.25, 0.50,0.75, also SpO_(2 critical) is the critical value of oxygen saturation,normally equal to 0.9, Average(HR) is the arithmetic average of theregistrations of the heart rate based on the time slot to which theregistrations belongs during the observation time, while k, λ and ϵ areparameters that can be set during configuration of the system S.

The dynamic evolution of the token can develop both on the main verticalpaths, as shown in FIG. 1, and in possible alternative paths, whichconnect them with each other, as shown in FIG. 2.

Transitions that precede a place all have the same condition.

Competitive situations or situations in which different conditions occursimultaneously belonging to several major chains are solved based on thepriority levels of the chains represented in FIG. 3.

A part of the chain corresponding to missing measurements, the sameinput is capable of activating simultaneously more than one transition.

In particular, starting from a place corresponding to the state ofhealth of said monitored patient, if the registration R made at adetermined hourly class shows a sudden decay of SpO₂ below apredetermined alarm threshold, associated with a small increase of HR,transitions corresponding to timely Oximetryscore Alarm, SpO₂ Alarm andAlarm Oximetry score chains would be activated simultaneously.

However, due to the different priority levels shown in FIG. 3, only onetransition corresponding to the chain alarm is activated, allowing thepassage of the token from one place to the next in a uniform way.

The table below shows all the possible transitions and the conditionsfor its activation:

Transitions Activation condition T₁, T₂, T₃₂, T₁₃, T₈, T₁₀ Missingmeasiring event T₀, T₂₀, T₂₁, T₃₁, T₁₁, T₁₂ Measure X_(SPO) ₂ < 90 T₂₈,T₂₉ X_(HR) > Average_((HR)) + K * Dev_(st(SPO) ₂ ₎ T₃, T₂₆ WOS(X_(SPO) ₂, X_(HR)) > WOS(SPO_(2critical) + ε, Average(HR)) * 0.25^(λ) T₄, T₂₂,T₂₇ WOS(X_(SPO) ₂ , X_(HR)) > WOS(SPO_(2critical) + ε, Average(HR)) *0.50^(λ) T₅, T₁₈, T₂₃, T₂₅ WOS(X_(SPO) ₂ , X_(HR)) >WOS(SPO_(2critical) + ε, Average(HR)) * 0.75^(λ) T₆, T₁₅, T₁₆, T₁₇, T₃₃WOS(X_(SPO) ₂ , X_(HR)) > WOS(SPO_(2critical) + ε, Average(HR)) T₁₃,T₁₄, T₉, T₇, T₃₀ WOS(X_(SPO) ₂ , X_(HR)) ≤ WOS(SPO_(2critical) + ε,Average(HR)) * 0.25^(λ)

As regards the self-learning of said BMSF, using a self-learningalgorithm, whose configuration parameters P (ϵ, Weight SpO₂, WeightHR,κ, λ) are learned on the basis of the labeling of alarm events by thedoctor during said initial training step of said monitored patient.

The learning procedure of the aforesaid parameters takes place accordingto the following steps:

-   -   at first, providing a set of validation of registrations R is        necessary, on which, once fixed a possible configuration of the        parameters {circumflex over (P)}, the algorithm is tested and        the weighted accuracy, called acc_(Weighted) is estimated.

The table below shows a matrix of weights for the calculation ofacc_(Weighted).

HR alarm, Oximetry Missing Oximetry punctual alarm Health data scorescore, SpO₂ state alarm alarm alarm Health state 1 1 1 1 Missing dataalarm 1 1 2 1 Oximetry score alarm 2 1 1 1 HR alarm, Oximetry 1 1 1 1punctual alarm score, SpO₂ alarm

Then parameters {circumflex over (P)} are tested.

Referring to the equation of the calculation of the value WOS(X_(SpO) ₂, X_(HR)), considering the configuration parameters Weight(X_(SpO) ₂ ),Weight(X_(HR)) due to the relative nature of the two weights, fixing oneof the two parameters and varying the other is sufficient.

It has been chosen to fix the parameter Weight(X_(HR)) equal to 1, whileWeight(X_(SpO) ₂ ) varies between 1 and 20.

The parameter λ∈[0.1 , . . . , i, . . . , 0.09]∪[1, . . . , j, . . .,10], with i ∈

and j ∈

and such that λ_(i+l) −λ₁=0.01.

The parameter κ∈[1, . . . , i, . . . 10], with i ∈

.

The set of parameters {circumflex over (P)} is given by the Cartesianproduct between the possible combinations of parameters P (ϵ, WeightSpO₂, WeightHR, κ, λ).

The configuration of the parameters used in said system S, indicated byP* is equal to the combination that maximizes the value of the accuracyweighing, or P*=arg max_(t) (acc_(weighted) ({circumflex over(P)}_(t))).

According to what is described, said system S has predictivecapabilities since the states of alert, prior to the alarm can beregarded as different levels of probability that the patient may have anexacerbation, sending alert signals even when the patient does not showsymptoms and the exacerbation is still in latent stage. Said system Sallows identifying the approaching of a dangerous event for saidmonitored patient, associated with the degeneration of the negativestate of health of the patient, as well as non-acquisition of themeasurements themselves.

Also, the use of the BMSF allows monitoring the health conditionsevolving over time.

The present invention has been described for illustrative but notlimitative purposes, according to its preferred embodiments, but it isto be understood that modifications and/or changes can be introduced bythose skilled in the art without departing from the relevant scope asdefined in the enclosed claims.

1. A system (S) for the detection and early warning of the incomingacute events in patients with chronic obstructive pulmonary disease,comprising: at least one device (D) for the detection of physiologicalparameters (R), that can be applied to said patient to be monitored; atleast one timer for detecting time intervals, such as date and time,associated with said detected physiological parameters (R); at least oneemission device of sound and/or visual alarm signals capable of emittingan sound and/or visual output warning signal, associated with saidphysiological detected parameters (R); a control logic unit (C),connectable to said at least one device (D) and at least one timer, andcapable of controlling said at least one emission signals device,suitable to receive in input said physiological detected parameters (R)and said time intervals, said control logic unit (C) being provided witha processing program, in which thresholds of predetermined valuesreached by said physiological parameters (R) are initially stored, whichruns the following steps: associating said detected physiologicalparameters (R) with the time intervals, in which the detection has takenplace; for every detection time instant, sending said physiologicalparameters (R) measured at a statistical indices calculation algorithm;comparing said statistical indexes obtained in the preceding step withsaid predetermined threshold and activating said at least one signalsemission device for the emission of a sound and/or visual warning signalif at least one of said statistical indexes exceeds said correspondingpredetermined threshold.
 2. The system(s) according to claim 1,characterized in that said at least one device (D) is a pulse oximeterthat detects the following physiological data: hemoglobin saturation(SpO); and heart rate (HR); in the following preset time frames, andscanned by said timer: morning hours interval (C_(Morning)); afternoonhours interval (C_(Afternoon)); evening hours interval (C_(Evening)). 3.The system(s) according to claim 2, characterized in that said logiccontrol unit (C) comprises a first unit (C) configured to carry out saidassociation of said detected physiological parameters (R) with the timeframes in which the detection has taken place, obtaining the followingregistrations: morning registrations (X_(SpO) ₂ ^(morning), X_(HR)^(morning)); afternoon registrations (X_(SpO) ₂ ^(afternoon), X_(HR)^(afternoon)); and evening registrations (X_(SpO) ₂ ^(evening), X_(HR)^(evening)); and a second unit (C₂), comprising a neural networkimplemented with a Binary Finite State Machine (BFSM), configured toprocess said grouped input data, according to said processing program.4. The system(s) according to claim 3, characterized in that said BinaryFinite State Machine (BMSF) runs a first calibration step for settingsaid predetermined thresholds of values P=(ϵ, Weight SpO₂, WeightHR, κ,λ), representing the typical trends of said physiological parameters ofhemoglobin saturation (SpO₂) and heart rate (HR) measurable from saidpatient to be monitored, and a second learning step of saidphysiological parameters of hemoglobin saturation (SpO₂) and heart rate(HR), wherein said Binary Finite State Machine (BMSF) learns the trendof said hemoglobin saturation (SpO₂) and heart rate (HR) of saidspecific patient to be monitored in said preset time frames of themorning (C_(Morning)), in the afternoon (C_(Afternoon)) and evening(C_(Evening)).
 5. The system(s) according to claim 3, characterized inthat said processing program performs the following steps for thecalculation of said statistical indices: calculation of the average ofsaid registrations of the morning (X_(SpO) ₂ ^(morning), X_(HR)^(morning)), afternoon (X_(SpO) ₂ ^(afternoon), X_(HR) ^(afternoon)) and(X_(SpO) ₂ ^(evening), X_(HR) ^(evening)), obtaining the values of theaverage of the morning (Average_(morning(SpO) ₂ ₎,Average_(morning(HR)), afternoon (Average_(afternoon(SpO) ₂ ₎,Average_(afternoon(HR)))and evening (Average_(evening(SpO) ₂ ),Average_(evening(HR))); calculating the standard deviation of saidregistrations of the morning (X_(SpO) ₂ ^(morning), X_(HR) ^(morning)),afternoon (X_(SpO) ₂ ^(afternoon), X_(HR) ^(afternoon)) and evening(X_(SpO) ₂ ^(evening), X_(HR) ^(evening)), obtaining the values of thestandard deviation of the morning (Devst_(morning(SpO) ₂ ₎,Devst_(morning(HR))), afternoon (Devst_(afternoon(SpO) ₂ ₎,Devst_(afternoon(HR))) and evening (Devst_(evening (SpO) ₂ ₎,Devst_(evening(HR))). calculating a weighted combination, or OximetryWeighted Score (WOS) for each class time:${{WOS}\left( {X_{{SPO}_{2}},X_{HR}} \right)} = \frac{\begin{matrix}{{- {{Weight}_{{SPO}_{2}}\left( \frac{X_{{SPO}_{2}} - {{Average}\left( {SPO}_{2} \right)}}{{Devst}\left( {SPO}_{2} \right)} \right)}} +} \\{{Weight}_{HR}\left( \frac{X_{HR} - {{Average}({HR})}}{{Devst}({HR})} \right)}\end{matrix}}{{Weight}_{{SPO}_{2}} + {Weight}_{HR}}$ comparing saidstatistical indexes obtained in previous steps with said predeterminedvalues thresholds.
 6. The system(s) according to claim 5, characterizedin that said Binary Finite State Machine (BMSF) evolves in the followingstates associated to critical warning events: HR Alarm when for twoconsecutive registrations (R) the condition X_(HR)

Average_(HR)+K*Devst_(HR) occurs, in which the value K is determined insaid self-learning step; Oximetryscore Punctual Alarm when the conditionWOS(X_(SpO) ₂ , X_(HR))>WOS(X_(SpO) ₂ , Average (HR)) occurs; SpO₂ Alarmwhen X_(SpO) ₂ ≤γ where γ∈[80,95] Missing Data Alarm in the case of twomissing consecutive registrations (R); Alarm Oximetry score associatedwith an alarm place reached when recording fulfils one of the followingconditions: Warning1: WOS(X_(SpO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ,Average(HR))*B₁ ^(λ) Warning2: WOS(X_(SpO) ₂ ,X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₂ ^(λ) Warning3:WOS(X_(SpO) ₂ , X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR))*B₃ ^(λ)AllarmWarrisomeOximaryScore:WOS(X_(SpO) ₂ ,X_(HR))>WOS(SPO_(2critical)+ϵ, Average(HR)) w herein B1, B2, B3 ϵ[0, 1]and SpO_(2 critical) is the critical value of oxygen saturation,normally equal to 0.9%.
 7. The system(s) according to claim 6,characterized in that said HR Alarm, Oximetryscore Punctual Alarm, SpO₂Alarm, Missing Data Alarm and Alarm Oximetry score states are providedas input to said emission device of sound and/or visual alarm signals,which emits sound and/or visual signals for each corresponding state. 8.The system(s) according to claim 1, characterized in that it comprises aplurality of sensors capable of detecting further physiologicalparameters from said monitored patient.
 9. The system(s) according toclaim 8, characterized in that said sensors comprise an accelerometerfor detecting movements of said patient and/or a spirometer for thedetection of pulmonary volume of said patient to provide input to saidcontrol logic unit (C).
 10. The system(s) according to claim 4,characterized in that in said calibration step of said Finite StateMachine (BMSF), a self-learning algorithm is used, which configurationparameters P=(ϵ, Weight SpO₂, WeightHR, κ, λ) can be set by a user. 11.The system(s) according to claim 4, characterized in that said FiniteState Machine (MSFB) learns said parameters P according to the followingsteps: providing a validation set of registrations (R) on which, fixed apossible configuration of the parameters {circumflex over (P)}, thealgorithm is tested and the weighted accuracy acc_(weighted) isestimated; testing said parameters, by setting the data value WeightHRequal to 1, while the value of the datum Weight SpO₂ can vary between 1and 20; determining the set of said parameters {circumflex over (P)} byperforming the Cartesian product of the possible combinations ofparameters P=(ϵ, Weight SpO₂, WeightHR, κ, λ); selecting the parameterconfiguration P* to be used by said system (S), by selecting acombination of said parameters that maximizes the weighted accuracyvalue according to the formula P*=arg max_(t) (acc_(weighted)({circumflex over (P)}_(t))).
 12. The system(s) according to claim 6,characterized in that said parameter λ∈[0.1, . . . , i, . . . ,0.09]∪[1, . . . , j, . . . , 10] with i ∈

and j ∈

and such that λ_(i+1)λ_(i)=0.01 and said parameter κ∈[1, . . . , i, . .. ,10] with i ∈

.